Transgenic Mice as a Model of Pre-Clinical Alzheimers Disease
Why this work is in the frame
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Bibliographic record
Abstract
At diagnosis, Alzheimer's disease (AD) brains are extensively burdened with plaques and tangles and display a degree of synaptic failure most likely beyond therapeutic treatment. It is therefore crucial to identify early pathological events in the progression of the disease. While it is not currently feasible to identify and study early, pre-clinical stages of AD, transgenic (Tg) models offer a valuable tool in this regard. Here we investigated cognitive, structural and biochemical CNS alterations occurring in our newly developed McGill-Thyl-APP Tg mice (over-expressing the human amyloid precursor protein with the Swedish and Indiana mutations) prior to extracellular plaque deposition. Pre-plaque, 3-month old Tg mice already displayed cognitive deficits concomitant with reorganization of cortical cholinergic pre-synaptic terminals. Conformational specific antibodies revealed the early appearance of intracellular amyloid β (Aβ)-oligomers and fibrillar oligomers in pyramidal neurons of cerebral cortex and hippocampus. At the same age, the cortical levels of insulin degrading enzyme -a well established Aβ-peptidase, were found to be significantly down-regulated. Our results suggest that, in the McGill-Thy1-APP Tg model, functional, structural and biochemical alterations are already present in the CNS at early, pre-plaque stages of the pathology. Accumulation of intraneuronal neurotoxic Aβ-oligomers (possibly caused by a failure in the clearance machinery) is likely to be the culprit of such early, pre-plaque pathology. Similar neuronal alterations might occur prior to clinical diagnosis in AD, during a yet undefined 'latent' stage. A better understanding of such pre-clinical AD might yield novel therapeutic targets and or diagnostic tools.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it